Stability Evaluation of Event Detection Techniques for Twitter
Stability Evaluation of Event Detection Techniques for Twitter
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DOI：
10.1007/978-3-319-46349-0_32
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Andreas Weiler;Jöran Beel;Bela Gipp;Michael Grossniklaus
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文献类型：
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作者：
Andreas Weiler;Jöran Beel;Bela Gipp;Michael Grossniklaus
Twitter作为最新新闻和信息的来源继续受到欢迎。因此，已经提出了许多事件检测技术来科普社交媒体数据流的稳定增长的速率和量。虽然大多数这些作品进行一些评估所提出的技术，比较它们的有效性是一项具有挑战性的任务。在本文中，我们研究的挑战再现事件检测技术的评估结果。我们应用了几种事件检测技术，并改变了四个参数，即时间窗口（15与30与60分钟），停止词（包括与排除），转发（包括与排除），以及定义事件的术语数量（1... 5项）。我们的实验使用真实世界的Twitter流数据，并表明单独改变这些参数会显著影响事件检测技术的结果，有时会以不可预见的方式。我们的结论是，即使是微小的变化，事件检测技术可能会导致重现实验的重大困难。
Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, comparing their effectiveness is a challenging task. In this paper, we examine the challenges to reproducing evaluation results for event detection techniques. We apply several event detection techniques and vary four parameters, namely time window (15 vs. 30 vs. 60 mins), stopwords (include vs. exclude), retweets (include vs. exclude), and the number of terms that define an event (1...5 terms). Our experiments use real-world Twitter streaming data and show that varying these parameters alone significantly influences the outcomes of the event detection techniques, sometimes in unforeseen ways. We conclude that even minor variations in event detection techniques may lead to major difficulties in reproducing experiments.